Overview

Dataset statistics

Number of variables26
Number of observations44850
Missing cells49251
Missing cells (%)4.2%
Duplicate rows619
Duplicate rows (%)1.4%
Total size in memory8.9 MiB
Average record size in memory208.0 B

Variable types

Categorical13
Numeric13

Warnings

Dataset has 619 (1.4%) duplicate rows Duplicates
Marque has a high cardinality: 51 distinct values High cardinality
Modèle dossier has a high cardinality: 458 distinct values High cardinality
Modèle UTAC has a high cardinality: 419 distinct values High cardinality
Désignation commerciale has a high cardinality: 3582 distinct values High cardinality
CNIT has a high cardinality: 44191 distinct values High cardinality
Type Variante Version (TVV) has a high cardinality: 28781 distinct values High cardinality
Puissance administrative is highly correlated with Puissance maximale (kW)High correlation
Puissance maximale (kW) is highly correlated with Puissance administrativeHigh correlation
Consommation urbaine (l/100km) is highly correlated with Consommation extra-urbaine (l/100km) and 2 other fieldsHigh correlation
Consommation extra-urbaine (l/100km) is highly correlated with Consommation urbaine (l/100km) and 2 other fieldsHigh correlation
Consommation mixte (l/100km) is highly correlated with Consommation urbaine (l/100km) and 2 other fieldsHigh correlation
CO2 (g/km) is highly correlated with Consommation urbaine (l/100km) and 2 other fieldsHigh correlation
NOX (g/km) is highly correlated with HC+NOX (g/km)High correlation
HC+NOX (g/km) is highly correlated with NOX (g/km)High correlation
masse vide euro min (kg) is highly correlated with masse vide euro max (kg)High correlation
masse vide euro max (kg) is highly correlated with masse vide euro min (kg)High correlation
Carburant is highly correlated with HybrideHigh correlation
Date de mise à jour is highly correlated with MarqueHigh correlation
Marque is highly correlated with Date de mise à jourHigh correlation
Hybride is highly correlated with CarburantHigh correlation
HC (g/km) has 34447 (76.8%) missing values Missing
HC+NOX (g/km) has 10659 (23.8%) missing values Missing
Particules (g/km) has 3142 (7.0%) missing values Missing
Particules (g/km) is highly skewed (γ1 = 87.095677) Skewed
CNIT is uniformly distributed Uniform
Type Variante Version (TVV) is uniformly distributed Uniform
Particules (g/km) has 19210 (42.8%) zeros Zeros

Reproduction

Analysis started2021-03-18 15:34:19.002581
Analysis finished2021-03-18 15:34:41.728971
Duration22.73 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Marque
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct51
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
MERCEDES-BENZ
38450 
VOLKSWAGEN
 
900
FIAT
 
607
OPEL
 
586
BMW
 
525
Other values (46)
 
3782

Length

Max length25
Median length13
Mean length12.02738016
Min length3

Characters and Unicode

Total characters539428
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowALFA-ROMEO
2nd rowALFA-ROMEO
3rd rowALFA-ROMEO
4th rowALFA-ROMEO
5th rowALFA-ROMEO
ValueCountFrequency (%)
MERCEDES-BENZ38450
85.7%
VOLKSWAGEN900
 
2.0%
FIAT607
 
1.4%
OPEL586
 
1.3%
BMW525
 
1.2%
SKODA364
 
0.8%
FORD296
 
0.7%
AUDI242
 
0.5%
CITROEN207
 
0.5%
MAZDA193
 
0.4%
Other values (41)2480
 
5.5%
2021-03-18T16:34:41.949735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mercedes-benz38450
84.9%
volkswagen900
 
2.0%
fiat607
 
1.3%
opel586
 
1.3%
bmw525
 
1.2%
skoda364
 
0.8%
ford296
 
0.7%
audi242
 
0.5%
citroen207
 
0.5%
mazda193
 
0.4%
Other values (45)2917
 
6.4%

Most occurring characters

ValueCountFrequency (%)
E157517
29.2%
S40939
 
7.6%
N40670
 
7.5%
D40087
 
7.4%
R40042
 
7.4%
M39980
 
7.4%
C39213
 
7.3%
B39092
 
7.2%
Z38674
 
7.2%
-38569
 
7.1%
Other values (18)24645
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter500422
92.8%
Dash Punctuation38569
 
7.1%
Space Separator437
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
E157517
31.5%
S40939
 
8.2%
N40670
 
8.1%
D40087
 
8.0%
R40042
 
8.0%
M39980
 
8.0%
C39213
 
7.8%
B39092
 
7.8%
Z38674
 
7.7%
A4594
 
0.9%
Other values (16)19614
 
3.9%
ValueCountFrequency (%)
-38569
100.0%
ValueCountFrequency (%)
437
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin500422
92.8%
Common39006
 
7.2%

Most frequent character per script

ValueCountFrequency (%)
E157517
31.5%
S40939
 
8.2%
N40670
 
8.1%
D40087
 
8.0%
R40042
 
8.0%
M39980
 
8.0%
C39213
 
7.8%
B39092
 
7.8%
Z38674
 
7.7%
A4594
 
0.9%
Other values (16)19614
 
3.9%
ValueCountFrequency (%)
-38569
98.9%
437
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII539428
100.0%

Most frequent character per block

ValueCountFrequency (%)
E157517
29.2%
S40939
 
7.6%
N40670
 
7.5%
D40087
 
7.4%
R40042
 
7.4%
M39980
 
7.4%
C39213
 
7.3%
B39092
 
7.2%
Z38674
 
7.2%
-38569
 
7.1%
Other values (18)24645
 
4.6%

Modèle dossier
Categorical

HIGH CARDINALITY

Distinct458
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
VIANO
14031 
VITO
9890 
SPRINTER
8323 
CLASSE E
2849 
CLASSE C
 
1302
Other values (453)
8455 

Length

Max length20
Median length5
Mean length5.99386845
Min length1

Characters and Unicode

Total characters268825
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)0.1%

Sample

1st row159
2nd row159
3rd row159
4th row159
5th row159
ValueCountFrequency (%)
VIANO14031
31.3%
VITO9890
22.1%
SPRINTER8323
18.6%
CLASSE E2849
 
6.4%
CLASSE C1302
 
2.9%
CADDY657
 
1.5%
CLASSE B434
 
1.0%
CLASSE S336
 
0.7%
CLASSE M277
 
0.6%
CLASSE A255
 
0.6%
Other values (448)6496
14.5%
2021-03-18T16:34:42.163599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
viano14031
26.5%
vito9890
18.7%
sprinter8323
15.7%
classe6346
12.0%
e2849
 
5.4%
c1302
 
2.5%
caddy657
 
1.2%
serie516
 
1.0%
b434
 
0.8%
s374
 
0.7%
Other values (408)8243
15.6%

Most occurring characters

ValueCountFrequency (%)
I34986
13.0%
O26212
9.8%
A25755
9.6%
V24719
9.2%
S24228
9.0%
N24010
8.9%
E20435
7.6%
T20343
7.6%
R19772
7.4%
C10206
 
3.8%
Other values (33)38159
14.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter257409
95.8%
Space Separator8115
 
3.0%
Decimal Number3091
 
1.1%
Dash Punctuation170
 
0.1%
Other Punctuation27
 
< 0.1%
Math Symbol13
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
I34986
13.6%
O26212
10.2%
A25755
10.0%
V24719
9.6%
S24228
9.4%
N24010
9.3%
E20435
7.9%
T20343
7.9%
R19772
7.7%
C10206
 
4.0%
Other values (17)26743
10.4%
ValueCountFrequency (%)
0813
26.3%
5568
18.4%
3363
11.7%
4319
 
10.3%
6276
 
8.9%
1212
 
6.9%
8196
 
6.3%
2170
 
5.5%
7119
 
3.8%
955
 
1.8%
ValueCountFrequency (%)
'20
74.1%
!6
 
22.2%
.1
 
3.7%
ValueCountFrequency (%)
8115
100.0%
ValueCountFrequency (%)
-170
100.0%
ValueCountFrequency (%)
+13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin257409
95.8%
Common11416
 
4.2%

Most frequent character per script

ValueCountFrequency (%)
I34986
13.6%
O26212
10.2%
A25755
10.0%
V24719
9.6%
S24228
9.4%
N24010
9.3%
E20435
7.9%
T20343
7.9%
R19772
7.7%
C10206
 
4.0%
Other values (17)26743
10.4%
ValueCountFrequency (%)
8115
71.1%
0813
 
7.1%
5568
 
5.0%
3363
 
3.2%
4319
 
2.8%
6276
 
2.4%
1212
 
1.9%
8196
 
1.7%
-170
 
1.5%
2170
 
1.5%
Other values (6)214
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII268810
> 99.9%
None15
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
I34986
13.0%
O26212
9.8%
A25755
9.6%
V24719
9.2%
S24228
9.0%
N24010
8.9%
E20435
7.6%
T20343
7.6%
R19772
7.4%
C10206
 
3.8%
Other values (32)38144
14.2%
ValueCountFrequency (%)
É15
100.0%

Modèle UTAC
Categorical

HIGH CARDINALITY

Distinct419
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
VIANO
14031 
VITO
9890 
SPRINTER
8323 
E 250
 
674
E 200
 
670
Other values (414)
11262 

Length

Max length20
Median length5
Mean length5.413333333
Min length1

Characters and Unicode

Total characters242788
Distinct characters41
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)< 0.1%

Sample

1st row159
2nd row159
3rd row159
4th row159
5th row159
ValueCountFrequency (%)
VIANO14031
31.3%
VITO9890
22.1%
SPRINTER8323
18.6%
E 250674
 
1.5%
E 200670
 
1.5%
CADDY657
 
1.5%
E 350528
 
1.2%
E 220460
 
1.0%
C 250360
 
0.8%
C 180336
 
0.7%
Other values (409)8921
19.9%
2021-03-18T16:34:42.398686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
viano14031
27.1%
vito9890
19.1%
sprinter8323
16.1%
e2849
 
5.5%
2501365
 
2.6%
c1302
 
2.5%
3501153
 
2.2%
2001119
 
2.2%
220889
 
1.7%
180760
 
1.5%
Other values (371)10129
19.6%

Most occurring characters

ValueCountFrequency (%)
I34253
14.1%
O25911
10.7%
V24664
10.2%
N23846
9.8%
T19979
8.2%
A19222
7.9%
R18837
7.8%
E12717
 
5.2%
S10689
 
4.4%
P8980
 
3.7%
Other values (31)43690
18.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter213503
87.9%
Decimal Number22135
 
9.1%
Space Separator6960
 
2.9%
Dash Punctuation162
 
0.1%
Other Punctuation26
 
< 0.1%
Math Symbol2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
I34253
16.0%
O25911
12.1%
V24664
11.6%
N23846
11.2%
T19979
9.4%
A19222
9.0%
R18837
8.8%
E12717
 
6.0%
S10689
 
5.0%
P8980
 
4.2%
Other values (16)14405
6.7%
ValueCountFrequency (%)
08758
39.6%
24516
20.4%
53482
 
15.7%
32191
 
9.9%
11043
 
4.7%
8985
 
4.4%
6584
 
2.6%
4420
 
1.9%
7101
 
0.5%
955
 
0.2%
ValueCountFrequency (%)
'20
76.9%
!6
 
23.1%
ValueCountFrequency (%)
6960
100.0%
ValueCountFrequency (%)
-162
100.0%
ValueCountFrequency (%)
+2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin213503
87.9%
Common29285
 
12.1%

Most frequent character per script

ValueCountFrequency (%)
I34253
16.0%
O25911
12.1%
V24664
11.6%
N23846
11.2%
T19979
9.4%
A19222
9.0%
R18837
8.8%
E12717
 
6.0%
S10689
 
5.0%
P8980
 
4.2%
Other values (16)14405
6.7%
ValueCountFrequency (%)
08758
29.9%
6960
23.8%
24516
15.4%
53482
 
11.9%
32191
 
7.5%
11043
 
3.6%
8985
 
3.4%
6584
 
2.0%
4420
 
1.4%
-162
 
0.6%
Other values (5)184
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII242788
100.0%

Most frequent character per block

ValueCountFrequency (%)
I34253
14.1%
O25911
10.7%
V24664
10.2%
N23846
9.8%
T19979
8.2%
A19222
7.9%
R18837
7.8%
E12717
 
5.2%
S10689
 
4.4%
P8980
 
3.7%
Other values (31)43690
18.0%

Désignation commerciale
Categorical

HIGH CARDINALITY

Distinct3582
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
VIANO 2.2 CDI
5874 
VIANO 2.0 CDI
3903 
COMBI 116 CDI
3754 
COMBI 113 CDI
 
2620
VIANO 3.0 CDI
 
1608
Other values (3577)
27091 

Length

Max length95
Median length17
Mean length20.09328874
Min length2

Characters and Unicode

Total characters901184
Distinct characters75
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2318 ?
Unique (%)5.2%

Sample

1st row159 1750 Tbi (200ch)
2nd row159 2.0 JTDm (170ch) ECO
3rd row159 2.0 JTDm (136ch)
4th row159 2.0 JTDm (136ch)
5th row159 2.0 JTDm (170ch)
ValueCountFrequency (%)
VIANO 2.2 CDI5874
 
13.1%
VIANO 2.0 CDI3903
 
8.7%
COMBI 116 CDI3754
 
8.4%
COMBI 113 CDI2620
 
5.8%
VIANO 3.0 CDI1608
 
3.6%
SPRINTER COMBI 213 CDI - 321548
 
3.5%
SPRINTER COMBI 213 CDI - 431258
 
2.8%
SPRINTER COMBI 213 CDI - 371111
 
2.5%
SPRINTER COMBI 316 CDI - 371074
 
2.4%
VIANO 2.2 CDI 4x4954
 
2.1%
Other values (3572)21146
47.1%
2021-03-18T16:34:42.646968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cdi33924
 
17.4%
combi18309
 
9.4%
viano14031
 
7.2%
sprinter8323
 
4.3%
7568
 
3.9%
2.26899
 
3.5%
2.05846
 
3.0%
fap4647
 
2.4%
bva74601
 
2.4%
1164468
 
2.3%
Other values (1264)86033
44.2%

Most occurring characters

ValueCountFrequency (%)
149799
16.6%
I86840
 
9.6%
C61975
 
6.9%
O40629
 
4.5%
D40130
 
4.5%
235781
 
4.0%
A35469
 
3.9%
134521
 
3.8%
N32274
 
3.6%
B31834
 
3.5%
Other values (65)351932
39.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter508615
56.4%
Decimal Number163815
 
18.2%
Space Separator149799
 
16.6%
Lowercase Letter40802
 
4.5%
Other Punctuation20717
 
2.3%
Dash Punctuation8166
 
0.9%
Open Punctuation4614
 
0.5%
Close Punctuation4612
 
0.5%
Math Symbol40
 
< 0.1%
Control4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
c4787
11.7%
x4390
10.8%
h4383
10.7%
i3587
8.8%
t3261
 
8.0%
e3221
 
7.9%
o2779
 
6.8%
r2499
 
6.1%
n1678
 
4.1%
a1598
 
3.9%
Other values (17)8619
21.1%
ValueCountFrequency (%)
I86840
17.1%
C61975
12.2%
O40629
 
8.0%
D40130
 
7.9%
A35469
 
7.0%
N32274
 
6.3%
B31834
 
6.3%
M25347
 
5.0%
R24727
 
4.9%
V24312
 
4.8%
Other values (16)105078
20.7%
ValueCountFrequency (%)
235781
21.8%
134521
21.1%
325310
15.5%
021916
13.4%
413607
 
8.3%
613148
 
8.0%
78344
 
5.1%
57479
 
4.6%
81997
 
1.2%
91712
 
1.0%
ValueCountFrequency (%)
.18748
90.5%
"582
 
2.8%
'566
 
2.7%
/565
 
2.7%
&250
 
1.2%
!6
 
< 0.1%
ValueCountFrequency (%)
149799
100.0%
ValueCountFrequency (%)
(4614
100.0%
ValueCountFrequency (%)
)4612
100.0%
ValueCountFrequency (%)
-8166
100.0%
ValueCountFrequency (%)
™4
100.0%
ValueCountFrequency (%)
+40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin549417
61.0%
Common351767
39.0%

Most frequent character per script

ValueCountFrequency (%)
I86840
15.8%
C61975
11.3%
O40629
 
7.4%
D40130
 
7.3%
A35469
 
6.5%
N32274
 
5.9%
B31834
 
5.8%
M25347
 
4.6%
R24727
 
4.5%
V24312
 
4.4%
Other values (43)145880
26.6%
ValueCountFrequency (%)
149799
42.6%
235781
 
10.2%
134521
 
9.8%
325310
 
7.2%
021916
 
6.2%
.18748
 
5.3%
413607
 
3.9%
613148
 
3.7%
78344
 
2.4%
-8166
 
2.3%
Other values (12)22427
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII901032
> 99.9%
None152
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
149799
16.6%
I86840
 
9.6%
C61975
 
6.9%
O40629
 
4.5%
D40130
 
4.5%
235781
 
4.0%
A35469
 
3.9%
134521
 
3.8%
N32274
 
3.6%
B31834
 
3.5%
Other values (62)351780
39.0%
ValueCountFrequency (%)
é146
96.1%
™4
 
2.6%
î2
 
1.3%

CNIT
Categorical

HIGH CARDINALITY
UNIFORM

Distinct44191
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
M10MCDVP324D533
 
16
M10FATVP001M592
 
16
M10FATVP001Z447
 
16
M10FATVP007R335
 
16
M10PELVP095G630
 
16
Other values (44186)
44770 

Length

Max length15
Median length15
Mean length14.99859532
Min length12

Characters and Unicode

Total characters672687
Distinct characters33
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44130 ?
Unique (%)98.4%

Sample

1st rowM10ALFVP000G340
2nd rowM10ALFVP000U221
3rd rowM10ALFVP000E302
4th rowM10ALFVP000F303
5th rowM10ALFVP000G304
ValueCountFrequency (%)
M10MCDVP324D53316
 
< 0.1%
M10FATVP001M59216
 
< 0.1%
M10FATVP001Z44716
 
< 0.1%
M10FATVP007R33516
 
< 0.1%
M10PELVP095G63016
 
< 0.1%
M10FATVP001215916
 
< 0.1%
M10FATVP001R14816
 
< 0.1%
M10FATVP001J85416
 
< 0.1%
M10FATVP001P59516
 
< 0.1%
M10MCDVP324229216
 
< 0.1%
Other values (44181)44690
99.6%
2021-03-18T16:34:42.922486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m10fatvp008m68216
 
< 0.1%
m10mcdvp324p06016
 
< 0.1%
m10mcdvp324s06316
 
< 0.1%
m10fatvp001m59216
 
< 0.1%
m10mcdvp324c53216
 
< 0.1%
m10pelvp095438816
 
< 0.1%
m10ladvp000t02816
 
< 0.1%
m10fatvp008n68316
 
< 0.1%
m10fatvp001r14816
 
< 0.1%
m10mcdvp324b53116
 
< 0.1%
Other values (44181)44690
99.6%

Most occurring characters

ValueCountFrequency (%)
089344
13.3%
M85652
12.7%
178432
11.7%
P48356
 
7.2%
V47974
 
7.1%
D41374
 
6.2%
C41171
 
6.1%
630537
 
4.5%
226049
 
3.9%
325210
 
3.7%
Other values (23)158588
23.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number352038
52.3%
Uppercase Letter320649
47.7%

Most frequent character per category

ValueCountFrequency (%)
M85652
26.7%
P48356
15.1%
V47974
15.0%
D41374
12.9%
C41171
12.8%
F9348
 
2.9%
G7326
 
2.3%
J4469
 
1.4%
R4222
 
1.3%
A3111
 
1.0%
Other values (13)27646
 
8.6%
ValueCountFrequency (%)
089344
25.4%
178432
22.3%
630537
 
8.7%
226049
 
7.4%
325210
 
7.2%
724096
 
6.8%
520897
 
5.9%
419993
 
5.7%
818997
 
5.4%
918483
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common352038
52.3%
Latin320649
47.7%

Most frequent character per script

ValueCountFrequency (%)
M85652
26.7%
P48356
15.1%
V47974
15.0%
D41374
12.9%
C41171
12.8%
F9348
 
2.9%
G7326
 
2.3%
J4469
 
1.4%
R4222
 
1.3%
A3111
 
1.0%
Other values (13)27646
 
8.6%
ValueCountFrequency (%)
089344
25.4%
178432
22.3%
630537
 
8.7%
226049
 
7.4%
325210
 
7.2%
724096
 
6.8%
520897
 
5.9%
419993
 
5.7%
818997
 
5.4%
918483
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII672687
100.0%

Most frequent character per block

ValueCountFrequency (%)
089344
13.3%
M85652
12.7%
178432
11.7%
P48356
 
7.2%
V47974
 
7.1%
D41374
 
6.2%
C41171
 
6.1%
630537
 
4.5%
226049
 
3.9%
325210
 
3.7%
Other values (23)158588
23.6%

Type Variante Version (TVV)
Categorical

HIGH CARDINALITY
UNIFORM

Distinct28781
Distinct (%)64.2%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
263AXG1B05
 
32
S-DKZ111AACD7A9BDA5
 
16
906AC35KNG71350NMCE21WA9
 
16
906AC35KNG71350EMCE21WA9
 
16
906AC35KMG71349EMCE21WA9
 
16
Other values (28776)
44754 

Length

Max length36
Median length22
Mean length21.13326644
Min length4

Characters and Unicode

Total characters947827
Distinct characters61
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19272 ?
Unique (%)43.0%

Sample

1st row939AXN1B52C
2nd row939AXP1B54C
3rd row939AXR1B64
4th row939AXR1B64B
5th row939AXS1B66
ValueCountFrequency (%)
263AXG1B0532
 
0.1%
S-DKZ111AACD7A9BDA516
 
< 0.1%
906AC35KNG71350NMCE21WA916
 
< 0.1%
906AC35KNG71350EMCE21WA916
 
< 0.1%
906AC35KMG71349EMCE21WA916
 
< 0.1%
312PXA1AP0F16
 
< 0.1%
S-DBZ111AACA7K0BDA516
 
< 0.1%
312PXG1AP1F16
 
< 0.1%
906AC35KHG71349EMCE21WA916
 
< 0.1%
906AC35KHG71350EMCE21WA916
 
< 0.1%
Other values (28771)44674
99.6%
2021-03-18T16:34:43.203935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
263axg1b0532
 
0.1%
906ac35khg71349nmce21wa916
 
< 0.1%
s-dbz111aaca7k0bda516
 
< 0.1%
906ac35kng71349emce21wa916
 
< 0.1%
s-dkz111aacd7a9bda516
 
< 0.1%
906ac35kmg71349nmce21wa916
 
< 0.1%
212140yzda16
 
< 0.1%
198axa1bg00e16
 
< 0.1%
169axh1a1216
 
< 0.1%
906ac35kng71350emce21wa916
 
< 0.1%
Other values (28772)44686
99.6%

Most occurring characters

ValueCountFrequency (%)
2113941
 
12.0%
3104452
 
11.0%
A92281
 
9.7%
164514
 
6.8%
060969
 
6.4%
951705
 
5.5%
640866
 
4.3%
N39517
 
4.2%
538376
 
4.0%
M36242
 
3.8%
Other values (51)304964
32.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number533521
56.3%
Uppercase Letter385673
40.7%
Other Punctuation24827
 
2.6%
Lowercase Letter1232
 
0.1%
Dash Punctuation924
 
0.1%
Open Punctuation819
 
0.1%
Close Punctuation819
 
0.1%
Space Separator12
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
A92281
23.9%
N39517
10.2%
M36242
 
9.4%
C27592
 
7.2%
K24379
 
6.3%
B21656
 
5.6%
L17880
 
4.6%
S14011
 
3.6%
F13428
 
3.5%
E12585
 
3.3%
Other values (16)86102
22.3%
ValueCountFrequency (%)
x480
39.0%
a292
23.7%
o164
 
13.3%
b84
 
6.8%
c76
 
6.2%
n75
 
6.1%
m9
 
0.7%
i7
 
0.6%
d7
 
0.6%
h6
 
0.5%
Other values (10)32
 
2.6%
ValueCountFrequency (%)
2113941
21.4%
3104452
19.6%
164514
12.1%
060969
11.4%
951705
9.7%
640866
 
7.7%
538376
 
7.2%
725261
 
4.7%
819985
 
3.7%
413452
 
2.5%
ValueCountFrequency (%)
(819
100.0%
ValueCountFrequency (%)
)819
100.0%
ValueCountFrequency (%)
-924
100.0%
ValueCountFrequency (%)
/24827
100.0%
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common560922
59.2%
Latin386905
40.8%

Most frequent character per script

ValueCountFrequency (%)
A92281
23.9%
N39517
10.2%
M36242
 
9.4%
C27592
 
7.1%
K24379
 
6.3%
B21656
 
5.6%
L17880
 
4.6%
S14011
 
3.6%
F13428
 
3.5%
E12585
 
3.3%
Other values (36)87334
22.6%
ValueCountFrequency (%)
2113941
20.3%
3104452
18.6%
164514
11.5%
060969
10.9%
951705
9.2%
640866
 
7.3%
538376
 
6.8%
725261
 
4.5%
/24827
 
4.4%
819985
 
3.6%
Other values (5)16026
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII947827
100.0%

Most frequent character per block

ValueCountFrequency (%)
2113941
 
12.0%
3104452
 
11.0%
A92281
 
9.7%
164514
 
6.8%
060969
 
6.4%
951705
 
5.5%
640866
 
4.3%
N39517
 
4.2%
538376
 
4.0%
M36242
 
3.8%
Other values (51)304964
32.2%

Carburant
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
GO
37778 
ES
6157 
EH
 
199
ES/GN
 
176
GN/ES
 
176
Other values (8)
 
364

Length

Max length5
Median length2
Mean length2.036923077
Min length2

Characters and Unicode

Total characters91356
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowES
2nd rowGO
3rd rowGO
4th rowGO
5th rowGO
ValueCountFrequency (%)
GO37778
84.2%
ES6157
 
13.7%
EH199
 
0.4%
ES/GN176
 
0.4%
GN/ES176
 
0.4%
GP/ES100
 
0.2%
ES/GP100
 
0.2%
GN59
 
0.1%
GH54
 
0.1%
EL39
 
0.1%
Other values (3)12
 
< 0.1%
2021-03-18T16:34:43.386557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
go37778
84.2%
es6157
 
13.7%
eh199
 
0.4%
gn/es176
 
0.4%
es/gn176
 
0.4%
es/gp100
 
0.2%
gp/es100
 
0.2%
gn59
 
0.1%
gh54
 
0.1%
el39
 
0.1%
Other values (3)12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G38444
42.1%
O37778
41.4%
E6961
 
7.6%
S6709
 
7.3%
/552
 
0.6%
N411
 
0.4%
H253
 
0.3%
P200
 
0.2%
L40
 
< 0.1%
F8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter90804
99.4%
Other Punctuation552
 
0.6%

Most frequent character per category

ValueCountFrequency (%)
G38444
42.3%
O37778
41.6%
E6961
 
7.7%
S6709
 
7.4%
N411
 
0.5%
H253
 
0.3%
P200
 
0.2%
L40
 
< 0.1%
F8
 
< 0.1%
ValueCountFrequency (%)
/552
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin90804
99.4%
Common552
 
0.6%

Most frequent character per script

ValueCountFrequency (%)
G38444
42.3%
O37778
41.6%
E6961
 
7.7%
S6709
 
7.4%
N411
 
0.5%
H253
 
0.3%
P200
 
0.2%
L40
 
< 0.1%
F8
 
< 0.1%
ValueCountFrequency (%)
/552
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII91356
100.0%

Most frequent character per block

ValueCountFrequency (%)
G38444
42.1%
O37778
41.4%
E6961
 
7.6%
S6709
 
7.3%
/552
 
0.6%
N411
 
0.4%
H253
 
0.3%
P200
 
0.2%
L40
 
< 0.1%
F8
 
< 0.1%

Hybride
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
non
44593 
oui
 
257

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters134550
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownon
2nd rownon
3rd rownon
4th rownon
5th rownon
ValueCountFrequency (%)
non44593
99.4%
oui257
 
0.6%
2021-03-18T16:34:43.527546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:34:43.575186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
non44593
99.4%
oui257
 
0.6%

Most occurring characters

ValueCountFrequency (%)
n89186
66.3%
o44850
33.3%
u257
 
0.2%
i257
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter134550
100.0%

Most frequent character per category

ValueCountFrequency (%)
n89186
66.3%
o44850
33.3%
u257
 
0.2%
i257
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin134550
100.0%

Most frequent character per script

ValueCountFrequency (%)
n89186
66.3%
o44850
33.3%
u257
 
0.2%
i257
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII134550
100.0%

Most frequent character per block

ValueCountFrequency (%)
n89186
66.3%
o44850
33.3%
u257
 
0.2%
i257
 
0.2%

Puissance administrative
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.01899666
Minimum1
Maximum81
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:43.642431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q19
median10
Q311
95-th percentile18
Maximum81
Range80
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.554474663
Coefficient of variation (CV)0.5040817088
Kurtosis26.20870961
Mean11.01899666
Median Absolute Deviation (MAD)1
Skewness4.399295434
Sum494202
Variance30.85218879
MonotocityNot monotonic
2021-03-18T16:34:43.743531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
912750
28.4%
1010180
22.7%
116699
14.9%
152806
 
6.3%
72404
 
5.4%
81683
 
3.8%
61269
 
2.8%
181193
 
2.7%
131088
 
2.4%
12876
 
2.0%
Other values (52)3902
 
8.7%
ValueCountFrequency (%)
136
 
0.1%
327
 
0.1%
4518
1.2%
5672
1.5%
61269
2.8%
ValueCountFrequency (%)
812
< 0.1%
802
< 0.1%
731
< 0.1%
721
< 0.1%
682
< 0.1%

Puissance maximale (kW)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct223
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.7808339
Minimum10
Maximum559.3
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:43.850627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile75
Q1100
median120
Q3125
95-th percentile195
Maximum559.3
Range549.3
Interquartile range (IQR)25

Descriptive statistics

Standard deviation49.15880391
Coefficient of variation (CV)0.3939611748
Kurtosis14.25184814
Mean124.7808339
Median Absolute Deviation (MAD)20
Skewness3.166187376
Sum5596420.4
Variance2416.588002
MonotocityNot monotonic
2021-03-18T16:34:44.215791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12014483
32.3%
1008783
19.6%
954125
 
9.2%
1652450
 
5.5%
1501345
 
3.0%
1901291
 
2.9%
125991
 
2.2%
70987
 
2.2%
140977
 
2.2%
225683
 
1.5%
Other values (213)8735
19.5%
ValueCountFrequency (%)
1021
< 0.1%
3511
< 0.1%
406
 
< 0.1%
431
 
< 0.1%
4416
< 0.1%
ValueCountFrequency (%)
559.34
< 0.1%
5412
< 0.1%
5152
< 0.1%
4863
< 0.1%
4762
< 0.1%
Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
M 6
19364 
A 5
13770 
A 7
8428 
M 5
 
1425
A 6
 
1101
Other values (11)
 
762

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters134550
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowM 6
2nd rowM 6
3rd rowM 6
4th rowM 6
5th rowM 6
ValueCountFrequency (%)
M 619364
43.2%
A 513770
30.7%
A 78428
18.8%
M 51425
 
3.2%
A 61101
 
2.5%
A 8446
 
1.0%
V 0180
 
0.4%
A 439
 
0.1%
D 539
 
0.1%
D 721
 
< 0.1%
Other values (6)37
 
0.1%
2021-03-18T16:34:44.407148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a23794
26.5%
m20801
23.2%
620477
22.8%
515234
17.0%
78461
 
9.4%
8446
 
0.5%
0191
 
0.2%
v180
 
0.2%
d71
 
0.1%
439
 
< 0.1%
Other values (3)6
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
44850
33.3%
A23794
17.7%
M20801
15.5%
620477
15.2%
515234
 
11.3%
78461
 
6.3%
8446
 
0.3%
0191
 
0.1%
V180
 
0.1%
D71
 
0.1%
Other values (4)45
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter44850
33.3%
Space Separator44850
33.3%
Decimal Number44850
33.3%

Most frequent character per category

ValueCountFrequency (%)
620477
45.7%
515234
34.0%
78461
18.9%
8446
 
1.0%
0191
 
0.4%
439
 
0.1%
12
 
< 0.1%
ValueCountFrequency (%)
A23794
53.1%
M20801
46.4%
V180
 
0.4%
D71
 
0.2%
N3
 
< 0.1%
S1
 
< 0.1%
ValueCountFrequency (%)
44850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common89700
66.7%
Latin44850
33.3%

Most frequent character per script

ValueCountFrequency (%)
44850
50.0%
620477
22.8%
515234
 
17.0%
78461
 
9.4%
8446
 
0.5%
0191
 
0.2%
439
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
A23794
53.1%
M20801
46.4%
V180
 
0.4%
D71
 
0.2%
N3
 
< 0.1%
S1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII134550
100.0%

Most frequent character per block

ValueCountFrequency (%)
44850
33.3%
A23794
17.7%
M20801
15.5%
620477
15.2%
515234
 
11.3%
78461
 
6.3%
8446
 
0.3%
0191
 
0.1%
V180
 
0.1%
D71
 
0.1%
Other values (4)45
 
< 0.1%

Consommation urbaine (l/100km)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct204
Distinct (%)0.5%
Missing42
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean9.706744331
Minimum0
Maximum41.1
Zeros1
Zeros (%)< 0.1%
Memory size350.5 KiB
2021-03-18T16:34:44.490992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.1
Q18.8
median9.8
Q310.7
95-th percentile13.8
Maximum41.1
Range41.1
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation2.366180698
Coefficient of variation (CV)0.2437666655
Kurtosis8.374190056
Mean9.706744331
Median Absolute Deviation (MAD)1
Skewness1.627844454
Sum434939.8
Variance5.598811098
MonotocityNot monotonic
2021-03-18T16:34:44.586709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.93012
 
6.7%
8.92542
 
5.7%
10.12511
 
5.6%
9.52492
 
5.6%
102165
 
4.8%
8.81939
 
4.3%
9.91750
 
3.9%
9.41686
 
3.8%
9.31589
 
3.5%
10.21557
 
3.5%
Other values (194)23565
52.5%
ValueCountFrequency (%)
01
 
< 0.1%
3.13
< 0.1%
3.21
 
< 0.1%
3.32
< 0.1%
3.43
< 0.1%
ValueCountFrequency (%)
41.12
 
< 0.1%
38.92
 
< 0.1%
25.62
 
< 0.1%
25.32
 
< 0.1%
25.228
0.1%

Consommation extra-urbaine (l/100km)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct91
Distinct (%)0.2%
Missing42
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean6.567634128
Minimum2.8
Maximum14.9
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:44.683358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.8
5-th percentile4.3
Q16.3
median6.7
Q37.1
95-th percentile8.4
Maximum14.9
Range12.1
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation1.196233739
Coefficient of variation (CV)0.1821407398
Kurtosis1.716265303
Mean6.567634128
Median Absolute Deviation (MAD)0.4
Skewness0.04820341419
Sum294282.55
Variance1.430975158
MonotocityNot monotonic
2021-03-18T16:34:44.788283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.54143
 
9.2%
6.73651
 
8.1%
6.63537
 
7.9%
6.93204
 
7.1%
7.12579
 
5.8%
6.82560
 
5.7%
72493
 
5.6%
6.41853
 
4.1%
7.41842
 
4.1%
7.31363
 
3.0%
Other values (81)17583
39.2%
ValueCountFrequency (%)
2.82
 
< 0.1%
2.96
 
< 0.1%
320
< 0.1%
3.115
< 0.1%
3.224
0.1%
ValueCountFrequency (%)
14.92
 
< 0.1%
14.52
 
< 0.1%
13.72
 
< 0.1%
11.911
< 0.1%
11.87
< 0.1%

Consommation mixte (l/100km)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct135
Distinct (%)0.3%
Missing39
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.716253822
Minimum1.2
Maximum24.5
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:44.885065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile5
Q17.2
median7.7
Q38.4
95-th percentile10.3
Maximum24.5
Range23.3
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.597110368
Coefficient of variation (CV)0.2069800197
Kurtosis4.204180919
Mean7.716253822
Median Absolute Deviation (MAD)0.6
Skewness0.8049484109
Sum345773.05
Variance2.550761527
MonotocityNot monotonic
2021-03-18T16:34:44.984927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.13179
 
7.1%
7.32535
 
5.7%
7.72474
 
5.5%
82416
 
5.4%
8.22232
 
5.0%
7.22109
 
4.7%
7.61841
 
4.1%
7.51799
 
4.0%
7.91672
 
3.7%
8.81625
 
3.6%
Other values (125)22929
51.1%
ValueCountFrequency (%)
1.22
 
< 0.1%
1.81
 
< 0.1%
2.11
 
< 0.1%
3.25
< 0.1%
3.312
< 0.1%
ValueCountFrequency (%)
24.52
 
< 0.1%
23.92
 
< 0.1%
172
 
< 0.1%
16.92
 
< 0.1%
16.48
< 0.1%

CO2 (g/km)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct264
Distinct (%)0.6%
Missing39
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean198.9108924
Minimum27
Maximum572
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:45.081730image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile128
Q1187
median203
Q3221
95-th percentile248.5
Maximum572
Range545
Interquartile range (IQR)34

Descriptive statistics

Standard deviation39.01467837
Coefficient of variation (CV)0.1961414878
Kurtosis1.838384712
Mean198.9108924
Median Absolute Deviation (MAD)16
Skewness-0.06819813963
Sum8913396
Variance1522.145128
MonotocityNot monotonic
2021-03-18T16:34:45.186951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2032402
 
5.4%
2162022
 
4.5%
2111858
 
4.1%
1931811
 
4.0%
1901682
 
3.8%
2131626
 
3.6%
2141528
 
3.4%
2321491
 
3.3%
2001491
 
3.3%
1981461
 
3.3%
Other values (254)27439
61.2%
ValueCountFrequency (%)
272
< 0.1%
481
< 0.1%
491
< 0.1%
791
< 0.1%
832
< 0.1%
ValueCountFrequency (%)
5722
 
< 0.1%
5552
 
< 0.1%
3972
 
< 0.1%
3932
 
< 0.1%
3888
< 0.1%

CO type I (g/km)
Real number (ℝ≥0)

Distinct593
Distinct (%)1.3%
Missing303
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.1534610434
Minimum0.005
Maximum0.968
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:45.291828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.042
Q10.046
median0.093
Q30.222
95-th percentile0.448
Maximum0.968
Range0.963
Interquartile range (IQR)0.176

Descriptive statistics

Standard deviation0.1389838203
Coefficient of variation (CV)0.9056619012
Kurtosis2.675773971
Mean0.1534610434
Median Absolute Deviation (MAD)0.047
Skewness1.595381261
Sum6836.2291
Variance0.01931650231
MonotocityNot monotonic
2021-03-18T16:34:45.394230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0467633
17.0%
0.0933531
 
7.9%
0.1753402
 
7.6%
0.0422700
 
6.0%
0.0782504
 
5.6%
0.0611801
 
4.0%
0.0061524
 
3.4%
0.3611286
 
2.9%
0.2271263
 
2.8%
0.3531213
 
2.7%
Other values (583)17690
39.4%
ValueCountFrequency (%)
0.0052
 
< 0.1%
0.0061524
3.4%
0.0148
 
< 0.1%
0.01613
 
< 0.1%
0.0210
 
< 0.1%
ValueCountFrequency (%)
0.9682
< 0.1%
0.9451
< 0.1%
0.941
< 0.1%
0.9312
< 0.1%
0.932
< 0.1%

HC (g/km)
Real number (ℝ≥0)

MISSING

Distinct71
Distinct (%)0.7%
Missing34447
Missing (%)76.8%
Infinite0
Infinite (%)0.0%
Mean0.03049897145
Minimum0.008
Maximum0.143
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:45.494092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.008
Q10.008
median0.031
Q30.044
95-th percentile0.06
Maximum0.143
Range0.135
Interquartile range (IQR)0.036

Descriptive statistics

Standard deviation0.01840769637
Coefficient of variation (CV)0.6035513822
Kurtosis-0.6453471212
Mean0.03049897145
Median Absolute Deviation (MAD)0.017
Skewness0.2741617162
Sum317.2808
Variance0.0003388432858
MonotocityNot monotonic
2021-03-18T16:34:45.598844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0082732
 
6.1%
0.0311264
 
2.8%
0.014914
 
2.0%
0.059385
 
0.9%
0.041359
 
0.8%
0.048329
 
0.7%
0.044300
 
0.7%
0.045213
 
0.5%
0.03209
 
0.5%
0.043203
 
0.5%
Other values (61)3495
 
7.8%
(Missing)34447
76.8%
ValueCountFrequency (%)
0.0082732
6.1%
0.01210
 
< 0.1%
0.01310
 
< 0.1%
0.014914
 
2.0%
0.01513
 
< 0.1%
ValueCountFrequency (%)
0.1434
< 0.1%
0.0842
 
< 0.1%
0.085
< 0.1%
0.0774
< 0.1%
0.0764
< 0.1%

NOX (g/km)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct219
Distinct (%)0.5%
Missing303
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.311837035
Minimum0.001
Maximum1.846
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:45.702869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.001
5-th percentile0.019
Q10.158
median0.197
Q30.228
95-th percentile1.843
Maximum1.846
Range1.845
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.4631117217
Coefficient of variation (CV)1.485108148
Kurtosis6.822629396
Mean0.311837035
Median Absolute Deviation (MAD)0.032
Skewness2.910705721
Sum13891.4044
Variance0.2144724668
MonotocityNot monotonic
2021-03-18T16:34:45.803930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1977626
17.0%
0.2263440
 
7.7%
1.8432732
 
6.1%
0.192692
 
6.0%
0.2242511
 
5.6%
0.2281802
 
4.0%
0.2111524
 
3.4%
0.2571248
 
2.8%
0.2531200
 
2.7%
0.031155
 
2.6%
Other values (209)18617
41.5%
ValueCountFrequency (%)
0.0018
 
< 0.1%
0.0027
 
< 0.1%
0.00335
 
0.1%
0.00424
 
0.1%
0.005457
1.0%
ValueCountFrequency (%)
1.846912
 
2.0%
1.8432732
6.1%
0.3428
 
< 0.1%
0.3228
 
< 0.1%
0.2751014
 
2.3%

HC+NOX (g/km)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct180
Distinct (%)0.5%
Missing10659
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean0.2247875991
Minimum0.038
Maximum0.306
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:45.908736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.038
5-th percentile0.156
Q10.201
median0.22
Q30.248
95-th percentile0.287
Maximum0.306
Range0.268
Interquartile range (IQR)0.047

Descriptive statistics

Standard deviation0.04168111184
Coefficient of variation (CV)0.1854244274
Kurtosis1.211501238
Mean0.2247875991
Median Absolute Deviation (MAD)0.028
Skewness-0.7510838011
Sum7685.7128
Variance0.001737315084
MonotocityNot monotonic
2021-03-18T16:34:46.012756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2167649
17.1%
0.2483331
 
7.4%
0.2012702
 
6.0%
0.2332520
 
5.6%
0.2871922
 
4.3%
0.2411800
 
4.0%
0.221525
 
3.4%
0.2631438
 
3.2%
0.2891258
 
2.8%
0.277798
 
1.8%
Other values (170)9248
20.6%
(Missing)10659
23.8%
ValueCountFrequency (%)
0.0381
 
< 0.1%
0.0461
 
< 0.1%
0.0532
< 0.1%
0.0541
 
< 0.1%
0.0663
< 0.1%
ValueCountFrequency (%)
0.30663
 
0.1%
0.3052
 
< 0.1%
0.3013
 
< 0.1%
0.29222
 
< 0.1%
0.2891258
2.8%

Particules (g/km)
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct16
Distinct (%)< 0.1%
Missing3142
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean0.0009607411048
Minimum0
Maximum0.61
Zeros19210
Zeros (%)42.8%
Memory size350.5 KiB
2021-03-18T16:34:46.098559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.001
Q30.001
95-th percentile0.004
Maximum0.61
Range0.61
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.006468898531
Coefficient of variation (CV)6.733238016
Kurtosis7997.747503
Mean0.0009607411048
Median Absolute Deviation (MAD)0.001
Skewness87.095677
Sum40.07059
Variance4.18466482 × 105
MonotocityNot monotonic
2021-03-18T16:34:46.173333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
019210
42.8%
0.00115382
34.3%
0.0023413
 
7.6%
0.0042774
 
6.2%
0.003849
 
1.9%
0.02232
 
0.1%
0.02516
 
< 0.1%
0.0078
 
< 0.1%
0.0236
 
< 0.1%
0.614
 
< 0.1%
Other values (6)14
 
< 0.1%
(Missing)3142
 
7.0%
ValueCountFrequency (%)
019210
42.8%
0.000442
 
< 0.1%
0.000682
 
< 0.1%
0.00115382
34.3%
0.001453
 
< 0.1%
ValueCountFrequency (%)
0.614
 
< 0.1%
0.431
 
< 0.1%
0.02516
< 0.1%
0.0236
 
< 0.1%
0.02232
0.1%

masse vide euro min (kg)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct859
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2070.96165
Minimum825
Maximum3115
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:46.266468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum825
5-th percentile1425
Q11976
median2076
Q32256
95-th percentile2586
Maximum3115
Range2290
Interquartile range (IQR)280

Descriptive statistics

Standard deviation342.8729755
Coefficient of variation (CV)0.1655622042
Kurtosis0.4119972036
Mean2070.96165
Median Absolute Deviation (MAD)110
Skewness-0.6321831902
Sum92882630
Variance117561.8773
MonotocityNot monotonic
2021-03-18T16:34:46.367985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21867392
16.5%
20766499
14.5%
25866085
13.6%
23564431
 
9.9%
19762726
 
6.1%
20251106
 
2.5%
20751096
 
2.4%
20501068
 
2.4%
1735975
 
2.2%
1845720
 
1.6%
Other values (849)12752
28.4%
ValueCountFrequency (%)
8258
< 0.1%
8395
 
< 0.1%
84514
< 0.1%
8554
 
< 0.1%
8702
 
< 0.1%
ValueCountFrequency (%)
31151
 
< 0.1%
29054
< 0.1%
28554
< 0.1%
28421
 
< 0.1%
27602
< 0.1%

masse vide euro max (kg)
Real number (ℝ≥0)

HIGH CORRELATION

Distinct937
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2169.545284
Minimum825
Maximum3115
Zeros0
Zeros (%)0.0%
Memory size350.5 KiB
2021-03-18T16:34:46.467911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum825
5-th percentile1435
Q12043.5
median2185
Q32355
95-th percentile2859
Maximum3115
Range2290
Interquartile range (IQR)311.5

Descriptive statistics

Standard deviation410.6005406
Coefficient of variation (CV)0.189256497
Kurtosis0.01978147978
Mean2169.545284
Median Absolute Deviation (MAD)170
Skewness-0.3528933368
Sum97304106
Variance168592.8039
MonotocityNot monotonic
2021-03-18T16:34:46.569886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21857459
16.6%
20756545
 
14.6%
23554437
 
9.9%
25852196
 
4.9%
26001348
 
3.0%
22751276
 
2.8%
25251074
 
2.4%
28591074
 
2.4%
23501048
 
2.3%
1735974
 
2.2%
Other values (927)17419
38.8%
ValueCountFrequency (%)
8258
< 0.1%
8395
 
< 0.1%
84514
< 0.1%
8554
 
< 0.1%
8702
 
< 0.1%
ValueCountFrequency (%)
31151
 
< 0.1%
309440
0.1%
308440
0.1%
30632
 
< 0.1%
30412
 
< 0.1%

Champ V9
Categorical

Distinct13
Distinct (%)< 0.1%
Missing235
Missing (%)0.5%
Memory size350.5 KiB
715/2007*692/2008EURO5
26426 
715/2007*566/2011EURO5
8250 
715/2007*630/2012EURO5
3332 
2005/55*2008/74EURO5
 
1876
2005/55*2008/74EEV
 
1830
Other values (8)
2901 

Length

Max length22
Median length22
Mean length21.75167545
Min length18

Characters and Unicode

Total characters970451
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row715/2007*692/2008EURO5
2nd row715/2007*692/2008EURO5
3rd row715/2007*692/2008EURO5
4th row715/2007*692/2008EURO5
5th row715/2007*692/2008EURO5
ValueCountFrequency (%)
715/2007*692/2008EURO526426
58.9%
715/2007*566/2011EURO58250
 
18.4%
715/2007*630/2012EURO53332
 
7.4%
2005/55*2008/74EURO51876
 
4.2%
2005/55*2008/74EEV1830
 
4.1%
715/2007*630/2012EURO61246
 
2.8%
715/2007*459/2012EURO51089
 
2.4%
715/2007*459/2012EURO6339
 
0.8%
715/2007*566/2011EURO6136
 
0.3%
715/2007*692/2008EURO686
 
0.2%
Other values (3)5
 
< 0.1%
(Missing)235
 
0.5%
2021-03-18T16:34:46.751925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
715/2007*692/2008euro526426
59.2%
715/2007*566/2011euro58250
 
18.5%
715/2007*630/2012euro53332
 
7.5%
2005/55*2008/74euro51876
 
4.2%
2005/55*2008/74eev1830
 
4.1%
715/2007*630/2012euro61246
 
2.8%
715/2007*459/2012euro51089
 
2.4%
715/2007*459/2012euro6339
 
0.8%
715/2007*566/2011euro6136
 
0.3%
715/2007*692/2008euro686
 
0.2%
Other values (3)5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0168641
17.4%
2121751
12.5%
5102819
10.6%
/89230
9.2%
785524
8.8%
163694
 
6.6%
649673
 
5.1%
E46445
 
4.8%
*44615
 
4.6%
U42785
 
4.4%
Other values (7)155274
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number659978
68.0%
Uppercase Letter176628
 
18.2%
Other Punctuation133845
 
13.8%

Most frequent character per category

ValueCountFrequency (%)
0168641
25.6%
2121751
18.4%
5102819
15.6%
785524
13.0%
163694
 
9.7%
649673
 
7.5%
830218
 
4.6%
927943
 
4.2%
45137
 
0.8%
34578
 
0.7%
ValueCountFrequency (%)
E46445
26.3%
U42785
24.2%
R42785
24.2%
O42783
24.2%
V1830
 
1.0%
ValueCountFrequency (%)
/89230
66.7%
*44615
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common793823
81.8%
Latin176628
 
18.2%

Most frequent character per script

ValueCountFrequency (%)
0168641
21.2%
2121751
15.3%
5102819
13.0%
/89230
11.2%
785524
10.8%
163694
 
8.0%
649673
 
6.3%
*44615
 
5.6%
830218
 
3.8%
927943
 
3.5%
Other values (2)9715
 
1.2%
ValueCountFrequency (%)
E46445
26.3%
U42785
24.2%
R42785
24.2%
O42783
24.2%
V1830
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII970451
100.0%

Most frequent character per block

ValueCountFrequency (%)
0168641
17.4%
2121751
12.5%
5102819
10.6%
/89230
9.2%
785524
8.8%
163694
 
6.6%
649673
 
5.1%
E46445
 
4.8%
*44615
 
4.6%
U42785
 
4.4%
Other values (7)155274
16.0%

Date de mise à jour
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
juin-13
43910 
mars-13
 
939
déc-12
 
1

Length

Max length7
Median length7
Mean length6.999977703
Min length6

Characters and Unicode

Total characters313949
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowjuin-13
2nd rowjuin-13
3rd rowjuin-13
4th rowjuin-13
5th rowjuin-13
ValueCountFrequency (%)
juin-1343910
97.9%
mars-13939
 
2.1%
déc-121
 
< 0.1%
2021-03-18T16:34:46.913190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:34:46.963321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
juin-1343910
97.9%
mars-13939
 
2.1%
déc-121
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
-44850
14.3%
144850
14.3%
344849
14.3%
j43910
14.0%
u43910
14.0%
i43910
14.0%
n43910
14.0%
m939
 
0.3%
a939
 
0.3%
r939
 
0.3%
Other values (5)943
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter179399
57.1%
Decimal Number89700
28.6%
Dash Punctuation44850
 
14.3%

Most frequent character per category

ValueCountFrequency (%)
j43910
24.5%
u43910
24.5%
i43910
24.5%
n43910
24.5%
m939
 
0.5%
a939
 
0.5%
r939
 
0.5%
s939
 
0.5%
d1
 
< 0.1%
é1
 
< 0.1%
ValueCountFrequency (%)
144850
50.0%
344849
50.0%
21
 
< 0.1%
ValueCountFrequency (%)
-44850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin179399
57.1%
Common134550
42.9%

Most frequent character per script

ValueCountFrequency (%)
j43910
24.5%
u43910
24.5%
i43910
24.5%
n43910
24.5%
m939
 
0.5%
a939
 
0.5%
r939
 
0.5%
s939
 
0.5%
d1
 
< 0.1%
é1
 
< 0.1%
ValueCountFrequency (%)
-44850
33.3%
144850
33.3%
344849
33.3%
21
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII313948
> 99.9%
None1
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
-44850
14.3%
144850
14.3%
344849
14.3%
j43910
14.0%
u43910
14.0%
i43910
14.0%
n43910
14.0%
m939
 
0.3%
a939
 
0.3%
r939
 
0.3%
Other values (4)942
 
0.3%
ValueCountFrequency (%)
é1
100.0%

Carrosserie
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
MINIBUS
32744 
BERLINE
5002 
BREAK
 
2271
TS TERRAINS/CHEMINS
 
1265
COUPE
 
1104
Other values (5)
 
2464

Length

Max length19
Median length7
Mean length7.427982163
Min length5

Characters and Unicode

Total characters333145
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBREAK
2nd rowBERLINE
3rd rowBERLINE
4th rowBERLINE
5th rowBERLINE
ValueCountFrequency (%)
MINIBUS32744
73.0%
BERLINE5002
 
11.2%
BREAK2271
 
5.1%
TS TERRAINS/CHEMINS1265
 
2.8%
COUPE1104
 
2.5%
COMBISPACE957
 
2.1%
CABRIOLET611
 
1.4%
MONOSPACE COMPACT610
 
1.4%
MINISPACE171
 
0.4%
MONOSPACE115
 
0.3%
2021-03-18T16:34:47.117122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:34:47.180767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
minibus32744
70.1%
berline5002
 
10.7%
break2271
 
4.9%
ts1265
 
2.7%
terrains/chemins1265
 
2.7%
coupe1104
 
2.4%
combispace957
 
2.0%
monospace725
 
1.6%
cabriolet611
 
1.3%
compact610
 
1.3%

Most occurring characters

ValueCountFrequency (%)
I74930
22.5%
B41585
12.5%
N41172
12.4%
S38392
11.5%
M36472
10.9%
U33848
10.2%
E18373
 
5.5%
R10414
 
3.1%
C7010
 
2.1%
A6610
 
2.0%
Other values (8)24339
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter330005
99.1%
Space Separator1875
 
0.6%
Other Punctuation1265
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
I74930
22.7%
B41585
12.6%
N41172
12.5%
S38392
11.6%
M36472
11.1%
U33848
10.3%
E18373
 
5.6%
R10414
 
3.2%
C7010
 
2.1%
A6610
 
2.0%
Other values (6)21199
 
6.4%
ValueCountFrequency (%)
1875
100.0%
ValueCountFrequency (%)
/1265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin330005
99.1%
Common3140
 
0.9%

Most frequent character per script

ValueCountFrequency (%)
I74930
22.7%
B41585
12.6%
N41172
12.5%
S38392
11.6%
M36472
11.1%
U33848
10.3%
E18373
 
5.6%
R10414
 
3.2%
C7010
 
2.1%
A6610
 
2.0%
Other values (6)21199
 
6.4%
ValueCountFrequency (%)
1875
59.7%
/1265
40.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII333145
100.0%

Most frequent character per block

ValueCountFrequency (%)
I74930
22.5%
B41585
12.5%
N41172
12.4%
S38392
11.5%
M36472
10.9%
U33848
10.2%
E18373
 
5.5%
R10414
 
3.1%
C7010
 
2.1%
A6610
 
2.0%
Other values (8)24339
 
7.3%

gamme
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size350.5 KiB
MOY-INFER
20428 
MOY-SUPER
15124 
LUXE
5223 
SUPERIEURE
 
1956
INFERIEURE
 
1884
Other values (2)
 
235

Length

Max length10
Median length9
Mean length8.50845039
Min length4

Characters and Unicode

Total characters381604
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMOY-SUPER
2nd rowMOY-SUPER
3rd rowMOY-SUPER
4th rowMOY-SUPER
5th rowMOY-SUPER
ValueCountFrequency (%)
MOY-INFER20428
45.5%
MOY-SUPER15124
33.7%
LUXE5223
 
11.6%
SUPERIEURE1956
 
4.4%
INFERIEURE1884
 
4.2%
ECONOMIQUE233
 
0.5%
MOY-INF2
 
< 0.1%
2021-03-18T16:34:47.377762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-18T16:34:47.437783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
moy-infer20428
45.5%
moy-super15124
33.7%
luxe5223
 
11.6%
superieure1956
 
4.4%
inferieure1884
 
4.2%
economique233
 
0.5%
moy-inf2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E52761
13.8%
R43232
11.3%
O36020
9.4%
M35787
9.4%
Y35554
9.3%
-35554
9.3%
I26387
6.9%
U26376
6.9%
N22547
5.9%
F22314
5.8%
Other values (6)45072
11.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter346050
90.7%
Dash Punctuation35554
 
9.3%

Most frequent character per category

ValueCountFrequency (%)
E52761
15.2%
R43232
12.5%
O36020
10.4%
M35787
10.3%
Y35554
10.3%
I26387
7.6%
U26376
7.6%
N22547
6.5%
F22314
6.4%
S17080
 
4.9%
Other values (5)27992
8.1%
ValueCountFrequency (%)
-35554
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin346050
90.7%
Common35554
 
9.3%

Most frequent character per script

ValueCountFrequency (%)
E52761
15.2%
R43232
12.5%
O36020
10.4%
M35787
10.3%
Y35554
10.3%
I26387
7.6%
U26376
7.6%
N22547
6.5%
F22314
6.4%
S17080
 
4.9%
Other values (5)27992
8.1%
ValueCountFrequency (%)
-35554
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII381604
100.0%

Most frequent character per block

ValueCountFrequency (%)
E52761
13.8%
R43232
11.3%
O36020
9.4%
M35787
9.4%
Y35554
9.3%
-35554
9.3%
I26387
6.9%
U26376
6.9%
N22547
5.9%
F22314
5.8%
Other values (6)45072
11.8%

Interactions

2021-03-18T16:34:25.075624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:25.226251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:25.350166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:25.509377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:25.615694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:25.713744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:25.808426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:25.907688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:26.007048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:26.096956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:26.215617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:26.361782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:26.472195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:26.571234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:26.675252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:26.770329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:26.852614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.035896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.135798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.221274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.308144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.391398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.477980image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.563605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.648744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.740628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.834443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:27.919123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.000332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.081880image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.162681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.256873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.344095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.436929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.526645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.609703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.692822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.787325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.873500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:28.958605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.044436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.129376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.212610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.302572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.393601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.485745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.572918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.659275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.745756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.836742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:29.919178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.120083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.222058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.305985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.387277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.473123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.559748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.645658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.742004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.829307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:30.911263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.002274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.085342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.166122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.250105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.333576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.418349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.508294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.600569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.687812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.779886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.867249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:31.952311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.054382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.138833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.224170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.310426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.394269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.476001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.563271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.654304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.742896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.843010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:32.939430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:33.021369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:33.113195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:33.206212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:33.300842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:33.397771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:33.486890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:33.571620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:33.657148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:33.903127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.016489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.110360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.202977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.290515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.387665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.477381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.571522image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.665220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.755629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.846929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:34.935556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.028513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.114331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.210097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.314892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.416909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.514199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.609676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.713105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.845671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:35.941896image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.026985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.111631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.197878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.283377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.367264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.457947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.551399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.644144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.731046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.822429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:36.913959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.006762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.098008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.181743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.272485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.367597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.454129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.549911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.641884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.738615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.853114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:37.934838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:38.018966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:38.100403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:38.184411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:38.279639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:38.380505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:38.693938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:38.809216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:38.911109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:38.993969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.085857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.169965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.259146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.351162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.434051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.517750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.600113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.690147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.783965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.884188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-18T16:34:39.972138image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-18T16:34:47.521934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-18T16:34:47.675831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-18T16:34:47.830727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-18T16:34:47.997365image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-18T16:34:48.173594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-18T16:34:40.234550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-18T16:34:40.980436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-18T16:34:41.331439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-18T16:34:41.546249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

MarqueModèle dossierModèle UTACDésignation commercialeCNITType Variante Version (TVV)CarburantHybridePuissance administrativePuissance maximale (kW)Boîte de vitesseConsommation urbaine (l/100km)Consommation extra-urbaine (l/100km)Consommation mixte (l/100km)CO2 (g/km)CO type I (g/km)HC (g/km)NOX (g/km)HC+NOX (g/km)Particules (g/km)masse vide euro min (kg)masse vide euro max (kg)Champ V9Date de mise à jourCarrosseriegamme
0ALFA-ROMEO159159159 1750 Tbi (200ch)M10ALFVP000G340939AXN1B52CESnon12147.0M 611.35.87.8182.00.6470.0520.032NaN0.00215051505715/2007*692/2008EURO5juin-13BREAKMOY-SUPER
1ALFA-ROMEO159159159 2.0 JTDm (170ch) ECOM10ALFVP000U221939AXP1B54CGOnon9125.0M 66.64.35.1136.00.192NaN0.1690.1900.00315651565715/2007*692/2008EURO5juin-13BERLINEMOY-SUPER
2ALFA-ROMEO159159159 2.0 JTDm (136ch)M10ALFVP000E302939AXR1B64GOnon7100.0M 66.64.25.1134.00.066NaN0.1490.1750.00115651565715/2007*692/2008EURO5juin-13BERLINEMOY-SUPER
3ALFA-ROMEO159159159 2.0 JTDm (136ch)M10ALFVP000F303939AXR1B64BGOnon7100.0M 66.64.25.1134.00.066NaN0.1490.1750.00115651565715/2007*692/2008EURO5juin-13BERLINEMOY-SUPER
4ALFA-ROMEO159159159 2.0 JTDm (170ch)M10ALFVP000G304939AXS1B66GOnon9125.0M 66.94.35.3139.00.060NaN0.1640.1930.00115651565715/2007*692/2008EURO5juin-13BERLINEMOY-SUPER
5ALFA-ROMEO159159159 2.0 JTDm (170ch)M10ALFVP000H305939AXS1B66BGOnon9125.0M 66.94.35.3139.00.060NaN0.1640.1930.00115651565715/2007*692/2008EURO5juin-13BERLINEMOY-SUPER
6ALFA-ROMEO159159159 1750 Tbi (200ch)M10ALFVP000H341939BXN1B53CESnon12147.0M 611.56.08.0186.00.6470.0520.032NaN0.00215551555715/2007*692/2008EURO5juin-13BREAKMOY-SUPER
7ALFA-ROMEO159159159 SW 2.0 JTDm (170ch) ECOM10ALFVP000S255939BXP1B55CGOnon9125.0M 66.74.45.2139.00.192NaN0.1690.1900.00316151615715/2007*692/2008EURO5juin-13BREAKMOY-SUPER
8ALFA-ROMEO159159159 SW 2.0 JTDm (136ch)M10ALFVP000J306939BXR1B65GOnon7100.0M 66.84.35.2137.00.066NaN0.1490.1750.00116151615715/2007*692/2008EURO5juin-13BREAKMOY-SUPER
9ALFA-ROMEO159159159 SW 2.0 JTDm (136ch)M10ALFVP000J307939BXR1B65BGOnon7100.0M 66.84.35.2137.00.066NaN0.1490.1750.00116151615715/2007*692/2008EURO5juin-13BREAKMOY-SUPER

Last rows

MarqueModèle dossierModèle UTACDésignation commercialeCNITType Variante Version (TVV)CarburantHybridePuissance administrativePuissance maximale (kW)Boîte de vitesseConsommation urbaine (l/100km)Consommation extra-urbaine (l/100km)Consommation mixte (l/100km)CO2 (g/km)CO type I (g/km)HC (g/km)NOX (g/km)HC+NOX (g/km)Particules (g/km)masse vide euro min (kg)masse vide euro max (kg)Champ V9Date de mise à jourCarrosseriegamme
44840VOLVOXC60XC60XC60 D4 (163ch) Geartronic 6M10VLVVP874H716DZ8850GOnon9120.0A 67.95.06.0159.00.280NaN0.1380.1590.00217251725715/2007*630/2012EURO5juin-13TS TERRAINS/CHEMINSSUPERIEURE
44841VOLVOXC60XC60XC60 D4 (163ch) Stop&Start BVM6M10VLVVP874J717DZ88A1GOnon9120.0M 66.44.65.3139.00.274NaN0.1170.1440.00017161716715/2007*630/2012EURO5juin-13TS TERRAINS/CHEMINSSUPERIEURE
44842VOLVOXC60XC60XC60 T6 (304ch) AWD Geartronic 6M1GVLVVP874Z718DZ90H6ESnon21224.0A 615.28.110.7249.00.3020.0400.029NaNNaN18121812715/2007*566/2011EURO5juin-13TS TERRAINS/CHEMINSSUPERIEURE
44843VOLVOXC70XC70XC70 D5 (215ch) AWD Geartronic 6M10VLVVP872R581BZ8256GOnon13158.0A 68.65.26.4169.00.344NaN0.1160.1520.00117981798715/2007*630/2012EURO5juin-13BREAKSUPERIEURE
44844VOLVOXC70XC70XC70 D5 (215ch) Stop&Start AWD BVM6M10VLVVP8723582BZ83A4GOnon12158.0M 66.14.85.3139.00.285NaN0.1060.1440.00017881788715/2007*630/2012EURO5juin-13BREAKSUPERIEURE
44845VOLVOXC70XC70XC70 D4 (163ch) AWD Geartronic 6M10VLVVP874P723BZ8756GOnon10120.0A 68.65.26.4169.00.344NaN0.1160.1520.00017991799715/2007*630/2012EURO5juin-13BREAKSUPERIEURE
44846VOLVOXC70XC70XC70 D4 (163ch) Stop&Start AWD BVM6M10VLVVP8725584BZ87A4GOnon9120.0M 66.14.85.3139.00.285NaN0.1060.1440.00017861786715/2007*630/2012EURO5juin-13BREAKSUPERIEURE
44847VOLVOXC70XC70XC70 D4 (163ch) Geartronic 6M10VLVVP8726585BZ8850GOnon9120.0A 67.74.85.9154.00.243NaN0.1080.1320.00117261726715/2007*630/2012EURO5juin-13BREAKSUPERIEURE
44848VOLVOXC70XC70XC70 D4 (163ch) Stop&Start BVM6M10VLVVP8727586BZ88A1GOnon9120.0M 66.84.55.3139.00.268NaN0.1270.1520.00117061706715/2007*630/2012EURO5juin-13BREAKSUPERIEURE
44849VOLVOXC70XC70XC70 T6 (304ch) AWD Geartronic 6M10VLVVP874P724BZ90H6ESnon21224.0A 615.18.110.6248.00.2190.0380.027NaNNaN18151815715/2007*566/2011EURO5juin-13BREAKSUPERIEURE